1. Field of the Art
The disclosure relates to the field of task management, and more particularly to the field of routing and assigning work tasks.
2. Discussion of the State of the Art
It is common for enterprises and corporations in the art to employ internal work routing systems, to assign work tasks to resources such as software systems or human personnel. Generally, these routing systems require the manual configuration of complex routing strategies, to instruct the system regarding how to assign work properly. Rules are configured and followed, and any change to routing must be effected by updating and rewriting these routing strategies.
Such arrangements can be very costly, in terms of resources (hardware and software resources must be dedicated to performing routing operations), time and money (employing someone to maintain the routing strategies). Additionally, such arrangements do not allow for adaptive behavior, and must be manually updated if any changes are desired. Again, this can be quite costly as changes to routing must now be performed manually, based on any observed results or metrics from prior routing decisions, which therefore requires personnel to review or monitor operations to determine if changes are needed.
What is needed is a means to automatically perform routing behavior with minimal manual input, and to provide adaptive routing behavior utilizing machine learning such that manual review and configuration is no longer needed.